import tensorflow as tf
from numpy.random import RandomState
batch_size=8
w1=tf.Variable(tf.random_normal([2,3],1,seed=1))
w2=tf.Variable(tf.random_normal([3,1],1,seed=1))
x=tf.placeholder(tf.float32,(None,2),name='x-input')
y_=tf.placeholder(tf.float32,(None,1),name='y-input')

a=tf.matmul(x,w1)
y=tf.matmul(a,w2)
cross_entropy=-tf.reduce_mean(y_*tf.log(tf.clip_by_value(y,1e-10,1.0)))
train_step=tf.train.AdamOptimizer(0.001).minimize(cross_entropy)

rdm=RandomState(1)
dataset_size=128
X=rdm.rand(dataset_size,2)
Y=[[int(x1+x2 < 1)] for (x1,x2) in X]
with tf.Session() as sess:
    sess.run(tf.initialize_all_variables())
    print(sess.run(w1))
    print(sess.run(w2))
    STEPs=5000
    for i in range(STEPs):
        start=(i*batch_size)%dataset_size
        end=min(start+batch_size,dataset_size)

        sess.run(train_step,feed_dict={x:X[start:end],y_:Y[start:end]})
        if i%1000==0:
            total_cross_entropy=sess.run(cross_entropy,feed_dict={x:X,y_:Y})
            print("after %d training step(s),cross entropy on all data is %g" % (i,total_cross_entropy))

    print(sess.run(w1))
    print(sess.run(w2))
